1 Resource Allocation in Multiple Access Channels Ali ParandehGheibi, Atilla Eryilmaz, Asuman Ozdaglar, and Muriel M´edard Abstract— We consider the problem of rate allocation in a Gaussian multiple-access channel, with the goal of maximizing a utility function over transmission rates. In contrast to the literature which focuses on linear utility functions, we study general concave utility functions. We present a gradient projection algorithm for this problem. Since the constraint set of the problem is described by exponentially many constraints, methods that use exact projections are computationally intractable. Therefore, we develop a new method that uses approximate projections. We use the polymatroid structure of the capacity region to show that the approximate projection can be implemented by a recursive algorithm in time polynomial in the number of users. We further propose another algorithm for implementing the approximate projections using rate-splitting and show improved bounds on its convergence time. I. I NTRODUCTION Dynamic allocation of communication resources such as bandwidth or transmission power is a central issue in multiple access channels in view of the time varying nature of the channel and interference effects. Most of the existing literature on resource allocation in multiple access channels focuses on specific communication schemes such as TDMA (time-division multiple access) [1] and CDMA (code-division multiple access) [2], [3] systems. An exception is the work by Tse et al. [4], who introduced the notion of throughput capacity for the fading channel with Channel State Information (CSI) and studied dynamic rate allocation policies with the goal of maximizing a linear utility function of rates over the throughput capacity region. In this paper, we consider the problem of rate allocation in a multiple access channel with perfect CSI. Contrary to the linear case in [4], we consider maximizing a general utility function of transmission rates over the capacity region. General concave utility functions allow us to model different performance metrics and fairness criteria (cf. Shenker [5], Srikant [6]). In view of space restrictions, we focus on the non-fading channel in this paper. In our companion paper [7], we extend our analysis to the fading channel. Our contributions can be summarized as follows. We introduce a gradient projection method for the problem of maximizing a concave utility function of rates over the capacity This research was partially supported by the National Science Foundation under grant DMI-0545910, and by DARPA ITMANET program. A. ParandehGheibi is with the Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge MA, 02139 (e-mail: parandeh@mit.edu) A. Eryilmaz is with the Electrical and Computer Engineering, Ohio State University, OH, 43210 (e-mail: eryilmaz@ece.osu.edu) A. Ozdaglar and M. M´edard are with the Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge MA, 02139 (e-mails: asuman@mit.edu, medard@mit.edu) region of a non-fading channel. We establish the convergence of the method to the optimal solution of the problem. Since the capacity region of the multiple-access channel is described by a number of constraints exponential in the number of users, the projection operation used in the method can be computationally expensive. To reduce the computational complexity, we introduce a new method that uses approximate projections. By exploiting the polymatroid structure of the capacity region, we show that the approximate projection operation can be implemented in polynomial time using submodular function minimization algorithms. Moreover, we present a more efficient algorithm for the approximate projection problem which relies on rate-splitting [8]. This algorithm also provides the extra information that allows the receiver to decode the message by successive cancelation. Other than the papers cited above, our work is also related to the work of Vishwanath et al. [9] which builds on [4] and takes a similar approach to the resource allocation problem for linear utility functions. Other works address different criteria for resource allocation including minimizing the weighted sum of transmission powers [10], and considering Quality of Service (QoS) constraints [11]. In contrast to this literature, we consider the utility maximization framework for general concave utility functions. The remainder of this paper is organized as follows: In Section II, we introduce the model and describe the capacity region of a multiple-access channel. In Section III, we consider the utility maximization problem in non-fading channel and present the gradient projection method. In Section IV, we address the complexity of the projection problem. Finally, we give our concluding remarks in Section V. Regarding the notation, we denote by xi the i-th component of a vector x. We denote the nonnegative orthant by Rn+ , i.e., Rn+ = {x ∈ Rn | x ≥ 0}. We write x0 to denote the transpose of a vector x.√We use kxk to denote the standard Euclidean norm, kxk = x0 x, and P(¯ x) to denote the exact projection of a vector x ¯ ∈ Rn on a nonempty closed convex set X, i.e., P(¯ x) = arg min k¯ x − xk. x∈X II. S YSTEM M ODEL We consider M users sharing the same media to communicate to a single receiver. We model the channel as a Gaussian multiple access channel with flat fading effects Y (n) = M p X Hi (n)Xi (n) + Z(n), (1) i=1 where Xi (n) are the transmitted waveform with average power Pi , Hi (n) is the channel gain corresponding to the i-th user and 2 Z(n) is white Gaussian noise with variance N0 . We assume that the channel gains are known to all users and the receiver 1 . We focus on the non-fading case when the channel gains are fixed. We assume without loss of generality that all channel gains are equal to unity. The capacity region of the Gaussian multiple-access channel is described as follows [12]: ½ ³X ´ X Cg (P ) = R ∈ RM Ri ≤ C Pi , N0 , + : i∈S i∈S ¾ for all S ⊆ M = {1, . . . , M } , (2) where Pi and Ri are the i-th user’s power and rate, respectively. C(P, N ) denotes Shannon’s formula for the capacity of an AWGN channel given by 1 P C(P, N ) = log(1 + ) nats. (3) 2 N III. R ESOURCE A LLOCATION IN N ON - FADING C HANNEL Consider the following utility maximization problem in a M -user non-fading multiple-access channel with channel gains fixed to unity. maximize u(R) subject to R ∈ Cg (P ), (4) where Ri and Pi are i-th user rate and power, respectively. The utility function u(R) is assumed to satisfy the following conditions. Assumption 1: (a) The utility function u(R) is concave with respect to vector R. (b) The utility function u(R) is monotonically non-decreasing with respect to Ri , for i = 1, . . . , M . (c) There exists a scalar B such that kgk ≤ B, ˜ kP(y) − y˜k ≤ ky − y˜k, for all y˜ ∈ X. (5) Proof: For part (i), it is straightforward to see that Pi (y) is given by (c.f. [13] Sec. 2.1.1) Pi (y) = y − ˜ P(y) = Pi1 (. . . (Pil−1 (Pil (y)))), is assumption is satisfied in practice when the receiver measures the channels and feeds back the channel information to the users. a0i y − bi ai . kai k Since ai just has non-negative entries, all components of y are decreased after projection and hence, the constraint i will not be violated in the subsequent projections. Given an infeasible vector y ∈ Rn , the approximate projection operation given in Definition 1 yields a feasible vector with respect to set X. Part (ii) can be verified using the nonexpansiveness property of projection on a convex set. Since y ˜ is a fixed point of Pi for all i, we have ˜ kP(y) − y˜k = kPi1 (. . . (Pil (y))) − Pi1 (. . . (Pil (˜ y )))k ≤ kPi2 (. . . (Pil (y))) − Pi2 (. . . (Pil (˜ y )))k .. . ≤ ky − y˜k. (6) Note that the result of approximate projection depends on the order of projections on violated constraints and hence it is not unique. The k-th iteration of the gradient projection method with approximate projection is given by for all g ∈ ∂u(R), where ∂u(R) denotes the subdifferential of u at R. The maximization problem in (4) is a convex program and the optimal solution can be obtained by several variational methods such as the gradient projection method. The gradient projection method with exact projection is typically user for problems where the projection operation is simple, i.e., for problems with simple constraint sets such as the non-negative orthant or a simplex. However, the constraint set in (4) is defined by exponentially many constraints, making the projection problem computationally intractable. To alleviate this problem, we use an approximate projection, which is obtained by successively projecting on some violated constraint. Definition 1: Let X = {x ∈ Rn |Ax ≤ b} where A has nonnegative entries. Let y ∈ Rn violate the constraint a0i x ≤ bi , for i ∈ {i1 , . . . , il }. The approximate projection of y on X, ˜ is given by denoted by P, 1 This where Pik denotes the exact projection on the hyperplane {x ∈ Rn |a0ik x = bik }. Proposition 1: The approximate projection P˜ given in Definition 1 has the following properties: ˜ (i) For any y ∈ Rn , P(y) is feasible with respect to set X, ˜ i.e., P(y) ∈ X. (ii) P˜ is pseudo-nonexpansive, i.e., ˜ k + αk g k ), Rk+1 = P(R g k ∈ ∂u(Rk ), (7) where g k is a subgradient at Rk , and αk denotes the stepsize. The following theorem provides a sufficient condition that can be used to establish convergence to the optimal solution. Theorem 1: Let Assumption 1 hold, and R∗ be an optimal solution of problem (4). Also, let the sequence {Rk } be generated by the iteration in (7). If the stepsize αk satisfies ³ ´ 2 u(R∗ ) − u(Rk ) 0 < αk < , (8) kg k k2 then kRk+1 − R∗ k < kRk − R∗ k. Proof: We have kRk + αk g k − R∗ k2 = (9) kRk − R∗ k2 + 2αk (Rk − R∗ )0 g k +(αk )2 kg k k2 . By concavity of u, we have (R∗ − Rk )0 g k ≥ u(R∗ ) − u(Rk ). (10) 3 Hence, kRk + αk g k − R∗ k2 ≤ kRk − R∗ k2 h ³ ´ i −αk 2 u(R∗ ) − u(Rk ) − (αk )kg k k2 . If the stepsize satisfies (8), the above relation yields the following kRk + αk g k − R∗ k < kRk − R∗ k. Now by applying pseudo-nonexpansiveness of the approximate projection we have kRk+1 − R∗ k ˜ k + αk g k ) − R∗ k = kP(R ≤ kRk + αk g k − R∗ k < kRk − R∗ k. Proposition 2: Let Assumption 1 hold. Also, let the sequence {Rk } be generated by the iteration in (7). If the stepsize αk satisfies (8), then {Rk } converges to an optimal solution R∗ . Proof: See Proposition 8.2.7 of [14]. The convergence analysis for this method can be extended for different stepsize rules. For instance, we can employ diminishing stepsize, i.e., αk → 0, ∞ X αk = ∞, k=0 or more complicated dynamic stepsize selection rules such as the path-based incremental target level algorithm proposed by Br¨annlund [15] which guarantees convergence to the optimal solution, and has better convergence rate compared to the diminishing stepsize rule. IV. C OMPLEXITY OF THE P ROJECTION P ROBLEM Even though the approximate projection is simply obtained by successive projection on the violated constraints, it requires to find the violated constraints among exponentially many constraints describing the constraint set. In this section, we exploit the special structure of the capacity constraints so that each gradient projection step in (7) can be performed in polynomial time in M . Definition 2: Let f : 2M → R be a function defined over all subsets of M. f is submodular if f (S∪T )+f (S∩T ) ≤ f (S)+f (T ), for all S, T ∈ 2M . (11) ¯ ∈ RM Proposition 3: For any R + , finding the most violated capacity constraint in (2) is equivalent to a submodular function minimization (SFM) problem. Proof: Define fC (S) : 2M → R as follows X fC (S) = C( Pi , N0 ), for all S ⊆ M. (12) i∈S It is straightforward to see that fC is a submodular function. We can rewrite the capacity constraints in (2) as X fC (S) − Ri ≥ 0, for all S ⊆ M. (13) i∈S ¯ is given by Thus, the most violated constraint at R X S ∗ = arg min fC (S) − Ri . S∈2M i∈S Since summation of a submodular and a linear function is also submodular, the problem above is of the form of submodular function minimization. It is first shown by Gr¨otschel et al. [16] that SFM problem can be solved in strongly polynomial time. The are several fully combinatorial strongly polynomial algorithms in the literature. The best known algorithm for SFM proposed by Orlin [17] has running time O(M 6 ) for the submodular function defined in (12). Note that approximate projection does not require any specific order for successive projections. Hence, finding the most violated constraint is not necessary for approximate projection. In view of this fact, a more efficient algorithm based on rate-splitting is presented in Appendix I, to find a violated constraint. This algorithm runs in O(M 2 log M ) time. Although a violated constraint can be obtained in polynomial time, it does not guarantee that the approximate projection can be performed in polynomial time. Because it is possible to have exponentially many constraints violated at some point and hence the total running time of the projection would be exponential in M . However, we show that for small enough stepsize in the gradient projection iteration (7), no more than M constraints can be violated at each iteration. Let us first define the notion of expansion for a polyhedron. Definition 3: Let Q be a polyhedron described by a set of linear constraints, i.e., Q = {x ∈ Rn : Ax ≤ b} . (14) Define the expansion of Q by δ, denoted by Eδ (Q), as the polyhedron obtained by relaxing all the constraints in (14), i.e., Eδ (Q) = {x ∈ Rn : Ax ≤ b + δ1} , where 1 is the vector of all ones. Lemma 1: Let fC be as defined in (12). There exists a positive scalar δ satisfying 1 (fC (S) + fC (T ) − fC (S ∩ T ) − fC (S ∪ T )), δ ≤ 2 for all S, T ∈ 2M , S ∩ T 6= S, T, (15) such that any point in the relaxed capacity region of an M user multiple-access channel, Eδ (Cg ), violates no more than M constraints of Cg defined in (2). Proof: Existence of a positive scalar δ satisfying (15) follows from submodularity of fC , and the fact that neither S nor T contains the other one. Suppose for some R ∈ Eδ (Cg ), there are M + 1 constraints of Cg violated. There are at least two violated constraints corresponding to some sets S, T ∈ 2M where S ∩ T 6= S, T . Because it is not possible to have M + 1 non-empty nested sets in 2M . We have X − Ri < −fC (S), (16) i∈S − X i∈T Ri < −fC (T ). (17) 4 Since R is feasible in the relaxed region, X Ri ≤ fC (S ∩ T ) + δ, (18) i∈S∩T X Ri ≤ fC (S ∪ T ) + δ. (19) i∈S∪T Note that if S ∩ T = ∅, (18) reduces to 0 ≤ δ which is a valid inequality. By summing the above inequalities we conclude 1 δ > (fC (S) + fC (T ) − fC (S ∩ T ) − fC (S ∪ T )), (20) 2 which is a contradiction. Theorem 2: Let Assumption 1 hold. Let P1 ≤ P2 ≤ . . . ≤ PM be the transmission powers. If the stepsize αk in the k-th iteration (7) satisfies h i 1 P1 P2 √ log 1 + αk ≤ , PM PM 4B M (N0 + i=3 Pi )(N0 + i=1 Pi ) then at most M constraints of the capacity region Cg can be violated at each iteration step. Proof: We first show that the inequality in (15) holds for the following choice of δ: i h 1 P1 P2 . δ = log 1 + PM PM 4 (N0 + i=3 Pi )(N0 + i=1 Pi ) In order to verify this, rewrite the right hand side of (15) as P h (N + P Pi ) i 1 0 i∈S Pi )(N0 + Pi∈T P log 4 (N0 + i∈S∩T Pi )(N0 + i∈S∪T Pi ) P i h 1 (i,j)∈(S\T )×(T \S) Pi Pj P P = log 1 + 4 (N0 + i∈S∩T Pi )(N0 + i∈S∪T Pi ) i h 1 P1 P2 P P ≥ log 1 + 4 (N0 + i∈S∩T Pi )(N0 + i∈S∪T Pi ) i h 1 P1 P2 ≥ log 1 + PM P 4 (N0 + i∈S∩T Pi )(N0 + i=1 Pi ) h i 1 P1 P2 ≥ log 1 + . PM PM 4 (N0 + i=3 Pi )(N0 + i=1 Pi ) The inequalities can be justified by using the monotonicity of the logarithm function and the fact that (S \ T ) × (T \ S) is non-empty because S ∩ T 6= S, T . Now, let Rk be feasible in the capacity region, Cg . For every S ⊆ M, we have X X X gk i (Rik + αk gik ) = Rik + αk kg k k kg k k i∈S i∈S i∈S ≤ X gk i f (S) + B B M i∈S kg k k ≤ f (S) + δ, δ √ (21) where the first inequality follows from the hypotheses and the second inequality follows from the fact that for any unit vector d ∈ RM , it is true that X X √ (22) di ≤ |di | ≤ M . i∈S i∈S Thus, if αk satisfies (21) then (Rk +αk g k ) ∈ Eδ (Cg ), for some δ for which (15) holds. Therefore, by Lemma 1 the number of violated constraints does not exceed M . In view of the fact that a violated constraint can be identified in O(M 2 log M ) time (see the Algorithm in Appendix I), Theorem 2 implies that, for small enough stepsize, the approximate projection can be implemented in O(M 3 log M ) time. V. C ONCLUSION We addressed the problem of optimal rate allocation in a nonfading multiple access channel from an information theoretic point of view. We formulated the problem as maximizing a general concave utility function of transmission rates over the capacity region of the multiple-access channel. We presented an iterative gradient projection method for solving this problem. In order to make the projection on a set defined by exponentially many constraints tractable, we considered a method that uses approximate projections. Using the special structure of the capacity region, we showed that the approximate projection can be performed in time polynomial in the number of users. In ongoing work, we extend our analysis to finding dynamic resource allocation policies in fading multiple-access channels. We study both rate and power allocation policies under different assumptions on the availability of channel statistics information. A PPENDIX I A LGORITHM FOR FINDING A VIOLATED CONSTRAINT In this section, we present an alternative algorithm based on rate-splitting idea to identify a violated constraint for an infeasible point. For a feasible point, the algorithm provides information for decoding by successive cancellation. We first introduce some definitions. Definition 4: The quadruple (M, P , R, N0 ) is called a configuration for an M -user multiple-access channel, where R = (R1 , . . . , RM ) is the rate tuple, P = (P1 , . . . , PM ) represents the received power and N0 is the noise variance. For any given configuration, the elevation, δ ∈ RM , is defined as the unique vector satisfying Ri = C(Pi , N0 + δi ), i = 1, . . . , M. (23) Intuitively, we can think of message i as rectangles of height Pi , raised above the noise level by δi . In face, δi is the amount of additional Gaussian interference that message i can tolerate. Note that if a configuration is feasible then its elevation vector is non-negative, but that is not sufficient to check feasibility. Definition 5: The configuration (M, P , R, N0 ) is singleuser codable, if after possible re-indexing, δi+1 ≥ δi + Pi , i = 0, 1, . . . , M − 1, (24) where we have defined δ0 = P0 = 0 for convention. By the graphical representation described earlier, a configuration is single-user codable if the none of the messages are overlapping. 5 Definition 6: The quadruple (m, p, r, N0 ) is a spin-off of (M, P , R, N0 ) if there exists a surjective mapping φ : {1, . . . , m} → {1, . . . , M } such that for all i ∈ {1, . . . , M } we have Pi ≥ X pj , j∈φ−1 (i) Ri ≤ X rj . j∈φ−1 (i) where φ−1 (i) is the set of all j ∈ {1, . . . , m} that map into i by means of φ. ¯ is obtained Definition 7: A hyper-user with power P¯ , rate R, by merging d actual users with powers (Pi1 , . . . , Pid ) and rates (Ri1 , . . . , Rid ), i.e, P¯ = d X Pik , ¯= R k=1 d X Rik . (25) k=1 Proposition 4: For any M -user achievable configuration (M, P , R, N0 ), there exists a spin-off (m, p, r, N0 ) which is single user codable. Proof: See Theorem 1 of [8]. Here, we give a brief sketch of the proof to give intuition about the algorithm. The proof is by induction on M . For a given configuration, if none of the messages are overlapping then the spin-off is trivially equal to the configuration. Otherwise, merge the two overlapping users into a hyper-user of rate and power equal the sum rate and sum power of the overlapping users, respectively. Now the problem is reduced to rate splitting for (M − 1) users. This proof suggests a recursive algorithm for rate-splitting that gives the actual spin-off for a given configuration. It follows directly from the proof of Proposition 4 that this recursive algorithm gives a single-user codable spin-off for an achievable configuration. If the configuration is not achievable, then the algorithm encounters a hyper-user with negative elevation. At this point the algorithm terminates. Suppose that hyper¯ and power P¯ . Negative elevation is equivalent user has rate R to the following ¯ > C(P¯ , N0 ). R Hence, by Definition 7 we have, X X Ri > C( Pi , N0 ). i∈S i∈S where S = {i1 , . . . , id } ⊆ M. Therefore, a hyper-user with negative elevation leads us to a violated constraint in the initial configuration. The complexity of this algorithm can be computed as follows. The algorithm terminates after at most M recursions. At each recursion, all the elevations are computed in O(M ) time and they are sorted in O(M log M ) time. Once the users are sorted by their elevation it takes O(M ) time to either find two overlapping users or a hyper-user with negative elevation. Hence, the algorithm runs in O(M 2 log M ) time. R EFERENCES [1] X. Wang and G.B. Giannakis. 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